chore(reset): v2.0.0 — storico certificato Deribit mainnet, ripartenza pulita
Reset del progetto su fondamenta verificate dopo la scoperta che l'intera libreria "validata OOS" era artefatto di feed contaminato (print fantasma del feed Cerbero TESTNET + storico Binance/USDT). - Storico ricostruito da Deribit MAINNET (ccxt pubblico, tokenless) e CERTIFICATO (certify_feed.py): BTC/ETH puliti su TUTTA la storia (mediana 2-6 bps vs Coinbase USD), integrita' OHLC + coerenza resample (maxΔ 0.00) + cross-venue OK. Alt esclusi (illiquidi/divergenti: LTC/DOGE 50-82% barre flat; XRP/BNB non certificabili). - Verdetto sul feed pulito: FADE / PAIRS / XS01 / TSM01 morti (ogni portafoglio Sharpe -2.3..-3.0, DD ~40%); solo SH01 e frammenti HONEST con segnale residuo, da ri-validare in isolamento. - Cleanup "restart pulito": strategie, stack live (src/live, src/portfolio, runner/executor, yml, docker), ~100 script ricerca/gate, waste/games/ portfolios, dati non certificati + cache e 60+ diari -> archiviati in Old/ (preservati, non cancellati). Diario consolidato in un unico documento. - Skeleton ricerca tenuto: Strategy ABC + indicatori + src/fractal + src/backtest/engine + load_data; tool dati certificati (rebuild_history, certify_feed, audit_feed, multi_source_check). - Universo dati ATTIVO: solo BTC/ETH (5m/15m/1h); guardrail fisico (load_data su alt -> FileNotFoundError). Esecuzione DISABILITATA, conto flat. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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Adriano Dal Pastro
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"""S2-01: Mean Reversion oraria con filtro orario.
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Idea: crypto ha bias di ritorno alla media nelle ore notturne (00-06 UTC)
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e di momentum nelle ore diurne USA (14-20 UTC).
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- Compra quando RSI < 30 in ore notturne
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- Vendi quando RSI > 70 in ore notturne
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- Hold max 4h, stop loss 1.5%
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Timeframe: 1h. Ingresso quasi giornaliero.
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"""
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from __future__ import annotations
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import sys
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sys.path.insert(0, ".")
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import numpy as np
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import pandas as pd
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from src.data.downloader import load_data
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FEE = 0.001
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INITIAL = 1000
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LEVERAGE = 3
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def rsi(close: np.ndarray, period: int = 14) -> np.ndarray:
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delta = np.diff(close)
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gain = np.where(delta > 0, delta, 0)
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loss = np.where(delta < 0, -delta, 0)
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result = np.full(len(close), 50.0)
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avg_gain = np.mean(gain[:period])
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avg_loss = np.mean(loss[:period])
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for i in range(period, len(delta)):
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avg_gain = (avg_gain * (period - 1) + gain[i]) / period
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avg_loss = (avg_loss * (period - 1) + loss[i]) / period
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if avg_loss == 0:
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result[i + 1] = 100
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else:
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rs = avg_gain / avg_loss
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result[i + 1] = 100 - 100 / (1 + rs)
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return result
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def bollinger_pct(close: np.ndarray, window: int = 20) -> np.ndarray:
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result = np.full(len(close), 0.5)
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for i in range(window, len(close)):
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w = close[i - window : i]
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ma = np.mean(w)
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std = np.std(w)
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if std > 0:
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result[i] = (close[i] - (ma - 2 * std)) / (4 * std)
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return result
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def run_mean_reversion(asset, tf="1h"):
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df = load_data(asset, tf)
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close = df["close"].values
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high = df["high"].values
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low = df["low"].values
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n = len(df)
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timestamps = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
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hours = timestamps.dt.hour.values
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rsi_vals = rsi(close, 14)
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bb_pct = bollinger_pct(close, 20)
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split = int(n * 0.7)
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configs = [
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# (rsi_low, rsi_high, allowed_hours, hold_max, stop_pct, name)
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(25, 75, list(range(0, 7)), 4, 0.015, "night_0-6_rsi25"),
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(30, 70, list(range(0, 7)), 4, 0.015, "night_0-6_rsi30"),
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(25, 75, list(range(0, 10)), 6, 0.02, "extended_0-9"),
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(30, 70, list(range(20, 24)) + list(range(0, 6)), 4, 0.015, "late_night"),
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(20, 80, list(range(0, 24)), 4, 0.015, "all_hours_rsi20"),
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# Bollinger band mean reversion
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]
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print(f"\n{'#'*60}")
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print(f" {asset} {tf} — MEAN REVERSION")
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print(f"{'#'*60}")
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for rsi_low, rsi_high, allowed, hold_max, stop, name in configs:
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capital = float(INITIAL)
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correct = 0
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total = 0
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daily_trades = {}
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for i in range(max(split, 20), n - hold_max):
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hour = hours[i]
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if hour not in allowed:
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continue
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day = timestamps[i].strftime("%Y-%m-%d")
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if daily_trades.get(day, 0) >= 2:
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continue
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direction = None
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if rsi_vals[i] < rsi_low and bb_pct[i] < 0.2:
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direction = "long"
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elif rsi_vals[i] > rsi_high and bb_pct[i] > 0.8:
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direction = "short"
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if direction is None:
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continue
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entry = close[i]
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best_exit = i + 1
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for j in range(i + 1, min(i + hold_max + 1, n)):
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price = close[j]
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if direction == "long":
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pnl_pct = (price - entry) / entry
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if pnl_pct <= -stop:
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best_exit = j
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break
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if pnl_pct >= stop * 1.5:
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best_exit = j
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break
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else:
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pnl_pct = (entry - price) / entry
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if pnl_pct <= -stop:
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best_exit = j
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break
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if pnl_pct >= stop * 1.5:
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best_exit = j
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break
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best_exit = j
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exit_price = close[best_exit]
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if direction == "long":
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trade_ret = (exit_price - entry) / entry
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else:
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trade_ret = (entry - exit_price) / entry
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net = trade_ret * LEVERAGE - FEE * 2 * LEVERAGE
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capital += capital * 0.15 * net
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capital = max(capital, 0)
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is_correct = trade_ret > 0
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total += 1
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if is_correct:
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correct += 1
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daily_trades[day] = daily_trades.get(day, 0) + 1
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if total < 20:
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continue
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acc = correct / total * 100
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ret = (capital - INITIAL) / INITIAL * 100
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test_days = (n - split) / 24
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test_years = test_days / 365.25
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ann = ((capital / INITIAL) ** (1 / test_years) - 1) * 100 if test_years > 0 and capital > 0 else -100
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dpnl = (capital - INITIAL) / test_days if test_days > 0 else 0
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days_with_trades = len(daily_trades)
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trades_per_day = total / days_with_trades if days_with_trades > 0 else 0
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tag = "✅" if acc >= 60 and ann >= 30 else ""
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print(f" {name:25s}: trades={total:5d} acc={acc:.1f}% ret={ret:+.1f}% ann={ann:+.1f}% dd_est ~{abs(min(0, ret/3)):.0f}% €/day={dpnl:.2f} days_active={days_with_trades} {tag}")
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for asset in ["ETH", "BTC"]:
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run_mean_reversion(asset, "1h")
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run_mean_reversion(asset, "15m")
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